Gradient Checking in Deep Learning Neural Networks

Thank you for your sharing and explanation! Indeed, Gradient Checking can effectively verify whether the gradient calculations in the backpropagation algorithm are correct. This technique is very useful when implementing deep learning models, as it helps us detect and correct issues in the code early on. For beginners, it is crucial to understand the processes of forward propagation, backpropagation, and gradient checking. The key points you mentioned—such as converting parameters and gradients into vector form for calculations, using small perturbations to approximate numerical gradients, and evaluating the reverse (comparing the differences between the two)—are essential for ensuring the correctness of the gradient computations.